Alike but not the same: Psychological profiles of COVID-19 vaccine skeptics

One of the challenges of the SARS-CoV-2 pandemic was a widespread skepticism about vaccination. To elucidate the underlying mental and emotional predispositions, we examined a sample of 1428 participants using latent profile analysis (LPA) on selected personality trait variables, mental health status, and measures of irrational beliefs. LPA revealed five distinct profiles: two classes of non-skeptics and three of skeptics. The smaller non-skeptic class reported the highest rates of mental health problems, along with high levels of neuroticism, hostility, interpersonal sensitivity, and external locus of control. The larger non-skeptic class was psychologically well-balanced. Conversely, the skeptic groups shared strong distrust of COVID-19 vaccination but differed in emotional and mental profiles, leading to graded differences in endorsing extreme conspiracy beliefs. This suggests that vaccine skepticism is not solely a result of mental illness or emotional instability; rather extreme skepticism manifests as a nuanced, graded phenomenon contingent on personality traits and conspirational beliefs.

Note.Due to a few missing values (max.8%), the sum of the percentages does not always equal 100%.
The percentages of vaccinated and unvaccinated persons were about equally distributed across men and women.

Exploratory Factor Analysis
The COVID-19 VABS was developed and psychometrically evaluated out of a pool of 27 items (see Table S2 for item wordings and Table S3 for sample statistics).To determine the number of factors in the data set, an exploratory factor analysis was conducted with increasing number of factors (see Table S4).Factor loadings on the first factor were almost always higher than loadings on the second and the third factor (see Table S5).

Table S2
Item pool for VABS

No.
Item wording 1 Data on COVID vaccine safety is often falsified.2 COVID vaccines are often advertised for profit reasons.3 Pharmaceutical companies cover up the dangers of COVID vaccines.4 I feel misinformed about the effectiveness of COVID vaccines.5 Data on the effectiveness of COVID vaccines are often falsified.6 I feel misinformed about the safety of COVID vaccines.7 The government is trying to cover up a link between COVID vaccines and cancer.8 COVID vaccines are harmful to health and this fact is being covered up.9r COVID vaccines are harmless, recoded (inverted item).10 The long-term consequences of COVID vaccines are still unexplored.11 When it comes to COVID vaccines, I feel helpless.12 It is better to get sick from COVID than to get the vaccine.13 Vaccines will not stop the COVID pandemic.14 I feel cheated, deceived, duped by those responsible for the COVID vaccines (e.g., government, pharmaceutical companies, etc.).15 I am apprehensive about the potential side effects from the COVID vaccine.16 I feel uncertain about the safety of the COVID vaccine.17 The As the descriptive statistics show, skewness and kurtosis values of the variables indicate that the variables are nonnormally distributed.However, the deviation from the normal distribution is not too large, so that the robust maximum likelihood estimator was used for the analyses.Almost all items load onto the first factor, while a clearly defined second factor does not seem to exist, apart from V26, the "Tiny Devices" item which was assessed separately anyway.
We additionally performed a principal component analysis followed by a parallel analysis in order to determine the number of factors.Principal component analysis revealed that there were only two eigenvalues > 1 (17.381,1.104, 0.694, …), but parallel analysis (1.276, 1.236, 1.204, …) suggested keeping only one factor.We, therefore, decided to keep only one factor.

Item Selection and Model Evaluation
As the EFA did not result in clearly separable factors, a single factor CFA using 27 items was performed using the robust maximum likelihood method (MLR) of the Mplus program, version 8.4 (Muthén & Muthén, 1998-2017).This estimator uses full information maximum likelihood (FIML) for handling missing data and takes non-normality of the data into account.

Extreme Conspiracy Beliefs
Two items (V26 and V27) not loading on the common VABS factor were nonetheless retained in the subsequent analyses as single items because of their high indication of conspiracy theory.These items had circulated in anti-vaxx social media channels among extreme conspiracists, i.e., "Tiny devices are placed in corona vaccines to track people's movements" and "Getting the COVID vaccine turns me into a guinea pig for genetic manipulation".Adding these two items to the VABS resulted in a worsened model fit, YB- 2 (104) = 656.90,p < .01,BIC = 55258.83compared to the model fit of the 14-item VABS with YB- 2 (77) = 296.350,p < .01,BIC = 49368.13.Additionally, this analysis led to 15 modification indices > 10 related solely to items 26 and 27, indicating that several error covariances should be freely estimated.Therefore, these items were used separately as a measure of extreme conspiracy beliefs together with the VABS in the latent profile analysis.

Gender Groups
Percentages of class membership for each gender group

!
Label and number of classes for latent class variable Classes V26_1 + V27_1)/2; !extreme conspiracy beliefs !All variables are rescaled to 7 response categories ANALYSIS: TYPE = MIXTURE; !Requests a mixture distribution analysis (LPA) STARTS = 500 100; !Increases the number of random starts in the !1st and 2nd step of the optimization STITERATIONS = 100; !Increases the number of iterations in the !1st step of the optimization LRTBOOTSTRAP = 30; !Increases the number of bootstrap draws !TECH11 = Requests the Lo-Mendell-Rubin likelihood ratio test of model fit !TECH14 = LR (k-1) difference test; SVALUES = Output of starting values OUTPUT:
Even the six-factor solution did not provide a good model fit.Below we show the factor loading matrix for three factors of the Mplus output.

Table S7 Correlation
Matrix of Vaccination Status and Measures used for LPA

Table S9
Average latent class probabilities for most likely latent class membership (row) by latent class

Table S10
Final class counts and proportions for the latent classes based on their most likely class

Latent Class Membership by Age Group Figure S2
Percentages of class membership for each age group